CFP last date
20 May 2024
Reseach Article

GA Based IFLC Design for an Industrial Process

by P. Subbaraj, P.S. Godwin Anand
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 11
Year of Publication: 2010
Authors: P. Subbaraj, P.S. Godwin Anand
10.5120/240-395

P. Subbaraj, P.S. Godwin Anand . GA Based IFLC Design for an Industrial Process. International Journal of Computer Applications. 1, 11 ( February 2010), 57-64. DOI=10.5120/240-395

@article{ 10.5120/240-395,
author = { P. Subbaraj, P.S. Godwin Anand },
title = { GA Based IFLC Design for an Industrial Process },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 11 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 57-64 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number11/240-395/ },
doi = { 10.5120/240-395 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:46:03.674933+05:30
%A P. Subbaraj
%A P.S. Godwin Anand
%T GA Based IFLC Design for an Industrial Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 11
%P 57-64
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy Logic with PID control (IFLC) has been applied for various applications which provide better performances compared to independent FLC, and PID. Although expert-system-based solutions are effective in controlling the processes. Design Fuzzy logic controller has traditionally been achieved through a process of trial and error. Such approach cannot obtain optimized FLC; more formal methods of knowledge base optimization are required. Genetic Algorithms (GAs) provide such a method to optimize the FLC parameters to globally optimum. In this paper, the FLC and the PID controller is optimally designed using the genetic algorithm. The effectiveness of the proposed approach (GAIFLC) is compared to a previous IFLC designed based on trial and error method and conventional PID controller for a three tank system. The simulation results of the proposed approach provide a satisfactory response in all means.

References
  1. B.G. Liptak: Instrumentation Engineer's Handbook: Process Control, Third Edition, CRC Press, 1995.
  2. Zadeh, Lotfi A., 1965, "Fuzzy sets. Information Control", 8, 338-353.
  3. Lee, C. C. "Fuzzy logic control systems: fuzzy logic controller part-I" IEEE Transactions on Systems, Man and Cybernetics, (1990). 20(2), 404-418.
  4. A.M.F. Fileti, A.J.B. Antunes, F.V. Silva, V. Silveira Jr, J.A.F.R. Pereira Experimental investigations on fuzzy logic for process control"Control Engineering Practice, Volume 15, Issue 9, September 2007, Pages 1149-1160.
  5. Sk.Faruque Ali, Ananth Ramaswamy, "Optimal fuzzy logic control for MDOF structural systems using Evolutionary algorithms" Engineering Applications of Artificial Intelligence 22(2009)407-419
  6. Godwin Anand and Albertraj, Proceedings of International Conference on Intelligent Knowledge Systems (IKS-2004), August 16-20,(2004) paper 36
  7. M. Suresh, Srinivasan, R. Rani Hemamalini "Integrated Fuzzy Logic Based Intelligent Control of Three Tank System" Serbian Journal of Electrical Engineering, Vol. 6, No. 1, May 2009, PP 1-14
  8. S.S. Patil and P. Bhaskar "International Journal of Electronic Engineering Research" Volume 1 Number 1 (2009) pp. 13-25
  9. Dragan.D.K,. Kuzmanovic.S.B,. Emil.L "Design of a PID-like compound fuzzy logic controller"Engineering Applications of Artificial Intelligence, 14 (2001) 785-803
  10. Shi.Yuhui.,. Eberhart.Russell,. Chen.Yaobin "Implementation of Evolutionary Fuzzy Systems" IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, April 1999
  11. Visioli.A "Tuning of PID controllers with fuzzy logic" IEEE Proc.-Control Theory Appl., Vol.148, No.1, January 2001
  12. Hyun-Joon.Cho,. Kwang-Bo.Cho,. Bo-Hyeun.Wang "Fuzzy-PID hybrid control: Automatic rule generation using genetic algorithms" Fuzzy Sets and Systems 92 (1997) 305-316
  13. Gurocak ,H.B. "A Genetic Algorithm method for tuning Fuzzy Logic Controllers" Fuzzy Sets and Systems 108 (1999) 305-316
  14. Chaiyaratana, N.; Zalzala, A.M.S. "Recent developments in evolutionary and genetic algorithms: theoryand applications"Genetic Algorithms in Engineering Systems: Innovations and Applications, Second International Conference On (Conf. Publ. No. 446) 2-4 Sep 1997 Page(s):270 - 277
  15. Cordon, O.; Alcala, R.; Alcala-Fdez, J.; Rojas, I. "Guest Editorial Genetic Fuzzy Systems: Whatapos;s Next? An Introduction to the Special Section", IEEE Trans. on Fuzzy Systems Volume 15, Issue 4, Aug. 2007 Page(s):533 - 535
  16. P.C. Chen, C.W. Chen and W.L. Chiang, "GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems", Expert Systems with Applications 36 (2009) 5872-5879
  17. Brad L. Miller and David E. Goldberg.1995, Genetic Algorithms, Tournament Selection and the Effects of Noise, vol 9, pp 193-212
  18. Arslan, A.,Kaya,M.,2001. Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets and Systems118,297-306.
  19. Zhao, Y.,CollinsJr.,E.G.,2003. Fuzzy parallel parking control of autonomous ground vehicles in tights paces. In: IEEE International Symposium on Intelligent Control, Houston,TX,pp.811-816.
  20. Sk.Faruque Ali, Ananth Ramaswamy, "Optimal fuzzy logic control for MDOF structural systems using Evolutionary algorithms" Engineering Applications of Artificial Intelligence 22(2009)407-419
Index Terms

Computer Science
Information Sciences

Keywords

FLC GA PID Optimization IFLC